User Location Information Research Articles

Efficient dummy generation for considering obstacles and protecting user location

SummaryTo receive location‐based services (LBS), users must disclose their locations and queries to the LBS server, which can expose the user's identity, location, and other information. Recently, techniques for protecting user privacy using dummies have been researched. However, many factors, such as the distance between the obstacles and the dummies, must be considered in order to create dummies. Therefore, this study proposes an efficient dummy creation technique to improve user privacy protection. Experimental results show that the proposed technique improves on other recent techniques.

Research on an Economic Localization Approach

With the development of wireless network and the wide application of pervasive computing technology, the location-based service (LBS) needs more and more location information for mobile users. At present, the outdoor positioning system based on satellite signals has been very mature, but it can not be applied in the complex indoor environment. Therefore, indoor positioning technology has rapidly become a research hotspot. At the same time, the rapid development of wireless network technology, because of its fast communication speed, easy deployment and other characteristics, WiFi-based indoor positioning technology has been widely concerned and studied. Therefore, this paper takes an economic WiFi-based indoor positioning method as the research foundation, and studies the corresponding improved algorithm aiming at the existing problems.

Towards privacy preservation for “check-in” services in location-based social networks

Location-based social networks (LBSNs) are convenient, but users must reveal their location information to network servers to enjoy an LBSN's services. However, user location information is sometimes sensitive; its disclosure can potentially cause severe privacy issues, for example, by revealing identity or health information. In this paper, we propose a k-anonymity-based algorithm that preserves user location information. To solve the location-privacy problem, the proposed algorithm, called the dummy-location selection algorithm, generates dummy locations that can be used to hide users’ actual locations. At the same time, because users may want to search through the check-in records of their friends, we design a novel algorithm that enables a quick search either by location or by friends’ social networks. We evaluate the performance of our proposed algorithm via extensive simulations.

Research on Key Technologies of Smart Campus Teaching Platform Based on 5G Network

With the development of mobile Internet technology and the popularity of intelligent mobile terminals, the data traffic load of mobile client users on the smart campus network platform has surged. How to reduce the data traffic of the smart campus network platform is an urgent problem to be solved. First, this paper discussed the key technologies of smart campus network teaching platform under the background of the 5G network, expounded the critical technologies of the transport layer of the Internet of Things (IoT) technology, and analyzed from the development perspective of the IoT platform. Second, by investigating the online classroom data of five types of colleges and universities in China and comparing the advantages and disadvantages of online classroom teaching and traditional classroom teaching, it is found that the number of online courses in colleges and universities has exploded in the second half of 2017. Next, this paper analyzed the demand of smart campus online teaching platform under the background of the 5G network, thus established an online teaching platform based on the four initiatives operation model (government-led, college sponsor, teacher subject, and academic director). Finally, this paper adopted the improved VIRE localization algorithm to obtain the specific location information of the student users in the classroom and, then, compared with the error obtained by the VIRE algorithm, and the error of the improved VIRE algorithm is smaller. In the process of obtaining information, the 5G network technology is used for data transmission, which can shorten the check-in time and can improve the positioning accuracy.

Predicting Quality of Service via Leveraging Location Information

QoS (Quality of Service) (our approach can be applied to a wide variety of services; in this paper, we focus on Web services) performance is intensively relevant to locations due to the network distance and the Internet connection between users and services. Thus, considering the location information of services and users is necessary. However, the location information has been ignored by most previous work. In this paper, we take both services’ and users’ location information into account. Specifically, we propose a location‐aware QoS prediction approach, called LANFM, by exploiting neural network techniques and factorization machine to improve user‐perceived experience. First of all, the information (e.g., id and location) of services and users is expressed as embedding vectors by leveraging neural network techniques. Then, the inner product of various embedding vectors, along with the weighted sum of feature vectors, is used to predict the QoS values. It should be noted that the inner product operation could capture the interactions between services and users, which is helpful to predict QoS values of services that have not been invoked by users. A collection of extensive experiments have been carried out on a real‐world dataset to validate the effectiveness of the LANFM model.

A Comprehensive Location-Privacy-Awareness Task Selection Mechanism in Mobile Crowd-Sensing

With the boom in the production of smartphones and easy access to networks, mobile crowd-sensing (MCS) is becoming a promising and rapidly growing sensing paradigm for the Internet of Things (IoT). In MCS, workers can get rewarded by participating in data collection while traveling to their destination from some starting point. However, the privacy leakage becomes a serious and unavoidable problem that hinders users' engagement. Taking task at a specific location or even going through some seemingly innocent locations may both disclose user's sensitive location information. Simply concealing or obfuscating the sensitive locations could preserve the privacy to a certain extent, at the cost of reward reduction, which will significantly frustrate the workers. To address these issues, this paper provides a novel framework for task selection in MCS jointly covering comprehensive location privacy preservation, efficient resource consumption, and high task profits. In our framework, workers can select their paths freely, and customize their privacy requirements for protection against both direct location disclosure and destination inference attack. Two corresponding algorithms are proposed for path selection under various stages of privacy preservation. The theoretical analysis demonstrates the performance of two algorithms on both privacy and utility aspects. We also evaluate our proposed mechanisms through extensive experiments on a real-world data set. The results validate that the proposed mechanisms can achieve higher task earnings and better privacy preservation.

A Spatial Consistency Model for Geometry-Based Stochastic Channels

Antennas with a massive amount of elements at one end are among 5G mobile communication key technologies for which spectral efficiency is enhanced by serving many users in parallel over tailored minimally interfering beams. This requires channel models that characterize the propagation environment in both azimuth and elevation. Additionally, the channel model has to capture spatial correlation effects among closely located positions, knowing that the propagation characteristics change gradually over the network area. In order to simulate mobile users or advanced beamforming strategies based on user location or angular information, it is crucial that spatial consistency is included in the applied channel models. This paper introduces a novel model for spatial consistency that is applicable to all prevalent geometry-based stochastic channel models. We provide a detailed explanation of the model and analyze its statistical properties and show its behavior when applied to the 3GPP 3D channel model as an example. To validate our model, we perform extensive ray-tracing simulations and show that our model is in a very good agreement with the statistical channel properties from ray-tracing. Following hypothesis testing over obtained ray-tracing statistics, we are able to parametrize our model for various 3GPP scenarios under LOS and NLOS propagation conditions. Finally, complementary aspects such as simulation complexity are discussed and a guideline on model implementation is provided.

Reversible spatio-temporal perturbation for protecting location privacy

Ubiquitous deployment of low-cost mobile positioning devices and widespread use of high-speed wireless networks have resulted in a rapid growth of location-based applications. While location-based services provide a wide range of life enhancing experiences to users, the exposure of location information poses significant privacy risks that can invade the users’ location privacy. Location privacy risks can be mitigated using location anonymization techniques that perturb the raw location of users to make the location indistinguishable from that of a set of other users. A fundamental limitation of traditional location anonymization techniques is that they are developed as unidirectional techniques that fail to support multi-level control of access to location data when data users have different access privileges on the exposed location information. As a result, location information once perturbed cannot be reduced in terms of anonymity or degree of perturbation even when some data users have access to fine granular information in the exposed data. Recent techniques on reversible spatial cloaking techniques employ data anonymization keys to perturb a user’s location in a pseudo-random manner such that the anonymized location information can be de-anonymized later using the anonymization keys. While reversible spatial cloaking provides support for multi-level location privacy, their performance is limited by their adopted spatial cloaking model in which the location perturbation occurs solely in the spatial domain without considering the temporal domain. Hence, reversible spatial location cloaking techniques obtain lower success rate and lower spatial resolution of the perturbed location leading to unreliable anonymization and lower service quality. This paper presents a new suite of reversible cloaking techniques that reversibly perturb location information of users using a spatio-temporal cloaking model, allowing data perturbation to occur along both spatial and temporal dimensions while still ensuring that the spatio-temporal expansion process is reversible when suitable access keys are provided. The proposed model achieves higher success rate and higher spatial resolution compared to reversible spatial cloaking. We compare our techniques through extensive experiments on real road networks. The results show that our techniques offer better QoS performance than the existing approaches and demonstrate strong attack resilience against adversarial attacks.

Unleashing the Power of Multi-Server PIR for Enabling Private Access to Spectrum Databases

The emergence of IoT is driving rapid growth in the use of wireless devices around the world. In addition, 5G, through its support for large numbers of devices, is expected to unleash the full potential of IoT globally. The growing number of wireless devices and the massive traffic stemming from the emergence of these technologies will result in a dramatic increase in the demand for spectrum resources. Dynamic spectrum sharing, enabled through cognitive radio network technology, emerges as a key solution for coping with these rising spectrum demands. One important approach that is currently being adopted as a potential solution for promoting dynamic spectrum sharing is the deployment and reliance on white space geo-location spectrum databases for locating spectrum availability in the TV white space. Despite the great benefits these databases offer in terms of their ability to help locate spectrum opportunities for secondary usage, they suffer from location privacy issues, as users need to reveal their location in the process of querying these databases for spectrum availability. Knowing that their whereabouts may be exposed, users can be discouraged from querying the databases, thereby hindering the adoption and deployment of this technology in future generation networks. In this article, we focus on the location privacy problem in database-driven dynamic spectrum access. Specifically, we present and compare key approaches that aim to protect the location information of secondary users in database-driven spectrum sharing and discuss some key research challenges that remain unaddressed.

An Online Context-Aware Machine Learning Algorithm for 5G mmWave Vehicular Communications

Millimeter-Wave (mmWave) bands have become the de-facto candidate for 5G vehicle-to-everything (V2X) since future vehicular systems demand Gbps links to acquire the necessary sensory information for (semi)-autonomous driving. Nevertheless, the directionality of mmWave communications and its susceptibility to blockage raise severe questions on the feasibility of mmWave vehicular communications. The dynamic nature of 5G vehicular scenarios and the complexity of directional mmWave communication calls for higher context-awareness and adaptability. To this aim, we propose an online learning algorithm addressing the problem of beam selection with environment-awareness in mmWave vehicular systems. In particular, we model this problem as a contextual multi-armed bandit problem. Next, we propose a lightweight context-aware online learning algorithm, namely fast machine learning (FML), with proven performance bound and guaranteed convergence. FML exploits coarse user location information and aggregates the received data to learn from and adapt to its environment. Furthermore, we demonstrate the feasibility of a real-world implementation of FML by proposing a standard-compliant protocol based on the existing architecture of cellular networks and the forthcoming features of 5G. We also perform an extensive evaluation using realistic traffic patterns derived from Google Maps. Our evaluation shows that FML enables mmWave base stations to achieve near-optimal performance on average within 33 mins of deployment by learning from the available context. Moreover, FML remains within ~ 5% of the optimal performance by swift adaptation to system changes (i.e., blockage, traffic).

Users' location analysis based on Chinese mobile social media

SummaryAfter the rapid development for more than 20 years, Internet has gradually become the main carrier of people's information and behaviors in people's daily life. In addition, the innovation and popularization of smartphone GPS makes user location information much more available and accurate, helping it to create remarkable values by which people are attracted to focus on social media–related data mining and applications. However, because of the sparsity of social media geographical information, direct inferences of locations have plenty of difficulties. Under the background of big data, this research has revised the UGC‐LI model in the preprocess of texts and the creation of the local dictionaries in which we take existed local dictionaries from the Internet into consideration, with the purpose of the inferences for users' and texts' locations. At the time of writing, through the crawler, we acquire users' personal information, the blog content, and customer relationships' (follows, fans) information more than 410 331 pieces from Sina Weibo. The experimental results show that the recall rate of the user location inference is 86.0%, whereas the precise rate is 77.4%, and the accuracy of text posted location inference is 66.8%. Compared with some other related algorithms, this revised model has comparatively better results in location inference for users and text publication.

Kyasanur Forest Disease Classification Framework Using Novel Extremal Optimization Tuned Neural Network in Fog Computing Environment.

Kyasanur Forest Disease (KFD) is a life-threatening tick-borne viral infectious disease endemic to South Asia and has been taking so many lives every year in the past decade. But recently, this disease has been witnessed in other regions to a large extent and can become an epidemic very soon. In this paper, a new fog computing based e-Healthcare framework has been proposed to monitor the KFD infected patients in an early phase of infection and control the disease outbreak. For ensuring high prediction rate, a novel Extremal Optimization tuned Neural Network (EO-NN) classification algorithm has been developed using hybridization of the extremal optimization with the feed-forward neural network. Additionally, a location based alert system has also been suggested to provide the global positioning system (GPS)-based location information of each KFD infected user and the risk-prone zones as early as possible to prevent the outbreak. Furthermore, a comparative study of proposed EO-NN with state of art classification algorithms has been carried out and it can be concluded that EO-NN outperforms others with an average accuracy of 91.56%, a sensitivity of 91.53% and a specificity of 97.13% respectively in classification and accurate identification of risk-prone areas.

Efficient and Privacy-Preserving Proximity Detection Schemes for Social Applications

With the pervasiveness of location-aware mobile terminals and the popularity of social applications, location-based social networking service (LBSNS) has brought great convenience to people's life. Meanwhile, proximity detection, which makes LBSNS more flexible, has aroused widespread concern. However, the prosperity of LBSNS still faces many severe challenges on account of users' location privacy and data security. In this paper, we propose two efficient and privacy-preserving proximity detection schemes, named arbitrary geometric range query for polygons (AGRQ-P) and arbitrary geometric range query for circles (AGRQ-C), for location-based social applications. With proposed schemes, a user can choose any area on the map, and query whether her/his friends are within the region without divulging the query information to both social application servers and other users, meanwhile, the accurate locations of her/his friends are also confidential for the servers and the query user. Specifically, with algorithms based on ciphertext of geometric range query, users' query and location information is blurred into ciphertext in client, thus no one but the user knows her/his own sensitive information. Detailed security analysis shows that various security threats can be defended. In addition, the proposed schemes are implemented in an IM APP with a real LBS dataset, and extensive simulation results over smart phones further demonstrate that AGRQ-P and AGRQ-C are highly efficient and can be implemented effectively.

The role of location and social strength for friendship prediction in location-based social networks

Recent advances in data mining and machine learning techniques are focused on exploiting location data. These advances, combined with the increased availability of location-acquisition technology, have encouraged social networking services to offer to their users different ways to share their location information. These social networks, called location-based social networks (LBSNs), have attracted millions of users and the attention of the research community. One fundamental task in the LBSN context is the friendship prediction due to its role in different applications such as recommendation systems. In the literature exists a variety of friendship prediction methods for LBSNs, but most of them give more importance to the location information of users and disregard the strength of relationships existing between these users. The contributions of this article are threefold, we: 1) carried out a comprehensive survey of methods for friendship prediction in LBSNs and proposed a taxonomy to organize the existing methods; 2) put forward a proposal of five new methods addressing gaps identified in our survey while striving to find a balance between optimizing computational resources and improving the predictive power; and 3) used a comprehensive evaluation to quantify the prediction abilities of ten current methods and our five proposals and selected the top-5 friendship prediction methods for LBSNs. We thus present a general panorama of friendship prediction task in the LBSN domain with balanced depth so as to facilitate research and real-world application design regarding this important issue.

Sistem Informasi Geografis Pencarian Lokasi Wisata Kuliner Terdekat di Kota Mataram Berbasis Website

Culinary development in the area of Mataram city at the moment is growing rapidly. Culinary tour offered varies greatly among them is the famous Lombok's cuisine with a taste of spiciness. The constraints experienced by travellers who have recently visited the city of Mataram is the difficulty to find a desired culinary location. Geographic information system for searching the location of nearby culinary Mataram city in this case can help make it easier for tourists to know the mapping of the location of the culinary tourism in the area of the city of Mataram. Web-based geographic information system is built by using the concept of MVC with directions from the position of travellers to culinary tour you want. The calculation to determine the distance of the location of culinary positions with travelers using the haversine formula. Based on a test that is done to the user, the average percentage of answers given by respondents agreed by 50% and the average percentage of answer strongly agree of 44.6% stating that geographic information systems this can already detect the locations of your users are able to display the distance between the user location information with location of culinary around, it can display the route to culinary tourism is selected and has the look of an attractive and easy to use. Keyword : Culinary, haversine, geographic information system, MVC.

A Method of Location Privacy Protection in Road Network Environment

With the widespread application of location-based services, users 'privacy concerns have become the focus of users' attention. Based on the k-anonymity method and the SpaceTwist algorithm, this paper proposes a method of incremental inquiry user privacy protection. The method preliminarily anonymizes the user's location information and points of interest on the client side, On the anonymous server side, combining the road network environment with the latitude and longitude grid generates the minimum anonymous area of random loop, instead of the user initiating incremental inquiry to the location service provider, Anonymous zones ensure k-anonymity for mobile users and road information to protect user privacy. Security and experimental analysis show that this scheme can improve the effectiveness of user query service while meeting the privacy requirements of users.

PRMS: A Personalized Mobile Search Over Encrypted Outsourced Data

At present, searchable encryption is a very promising direction in the field of cloud computing. However, most existing works focus on keyword-based search schemes and regardless of personalized search needs, a keyword-based search does not take into account the user’s location information and cannot exactly match users’ search intentions. Since location information is very important in mobile searches, we propose a personalized mobile search (PRMS) over encrypted outsourced data, and we convert the user’s location information into distance information to generate the user location model, forming a location query matrix with user location information. The matrix is then used to encrypt the user’s location query and conceal the location information in the location query matrix. In this paper, we combine a user’s interest preference and location information in personalized searches over encrypted outsourced data, adopt the law of universal gravitation to calculate scores of files in the cloud, and return the first $K$ results with the highest gravitational forces. For the first time, we propose a PRMS-improvement scheme that is applied to an encryption method to build the content index and location index, which can greatly reduce the time for model construction. Through the PRMS scheme, we realized personalized mobile searches based on user’s interests and location information.

Compromising location privacies for vehicles cloud computing

In this paper, we propose an enhanced vehicular crowdsourcing localisation and tracking (EVCLT) scheme for mounting a trajectory tracking attack in vehicular cloud computing environment. In our scheme, crowdsourcing technique is applied to sample the location information of certain users. Then matrix completion technique is used to generate our predictions of the users' trajectories. To alleviate the error disturbance of the recovered location data, Kalman filter technique is implemented and the trajectories of certain users are recovered with accuracy. At last, extensive simulations are conducted to show the performance of our scheme. Simulation results reveal that the proposed approach is able to accurately track the trajectories of certain users.

Compromising Location Privacies for Vehicles Cloud Computing

In this paper, we propose an enhanced vehicular crowdsourcing localisation and tracking (EVCLT) scheme for mounting a trajectory tracking attack in vehicular cloud computing environment. In our scheme, crowdsourcing technique is applied to sample the location information of certain users. Then matrix completion technique is used to generate our predictions of the users' trajectories. To alleviate the error disturbance of the recovered location data, Kalman filter technique is implemented and the trajectories of certain users are recovered with accuracy. At last, extensive simulations are conducted to show the performance of our scheme. Simulation results reveal that the proposed approach is able to accurately track the trajectories of certain users.

Location-based clustering and collaborative filtering for mobile learning

This paper presents a new m-learning model described as location-based collaborative M-learning (LCM). This new model exploits location information of mobile users and implements two main operations; k-means for clustering mobile users, and location-based collaborative filtering (CF) to provide learning items recommendations to clustered users. A comprehensive evaluation methodology was utilised to validate the proposed model in terms of complexity, performance and learning items' recommendations quality. Results have confirmed successful implementation of the proposed LCM model during different mobile users' settings. It was shown that LCM structure efficiently reduces processing overload and time required for users' clustering and learning content authoring, and improves learning items recommendations' accuracy.